COVID-19 Detection via Wavelet Entropy and Biogeography-Based Optimization

Yao, Xujing; Han, Ji

doi:10.1007/978-981-15-9682-7_8

Cited by 19 publications

(20 citation statements)

References 32 publications

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“…We compared our proposed DFMPNN method with 10 state-of-the-art methods: WEBBO ( 8 ), 3SBBO ( 9 ), DeCovNet ( 10 ), FSVC ( 11 ), GN-COD ( 12 ), GLCMSVM ( 13 ), 5L-DCNN ( 14 ), CSSN ( 15 ), FCONet ( 16 ), COVNet ( 17 ). All the comparison was carried on over the same test set of 10 runs.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

“…Therefore, scholars nowadays favor using artificial intelligence (AI) and modern deep learning (DL) to assist radiologists in recognizing COVID-19. Yao (8)…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, scholars nowadays favor using artificial intelligence (AI) and modern deep learning (DL) to assist radiologists in recognizing COVID-19. Yao ( 8 ) proposed a wavelet entropy biogeography-based optimization (WEBBO) method for COVID-19 diagnosis. Wu ( 9 ) presented three-segment biogeography-based optimization (3SBBO) for recognizing COVID-19 patients.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

Wang

Satapathy

Anderson

et al. 2021

Front. Public Health

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Aim: Coronavirus disease 2019 (COVID-19) is a form of disease triggered by a new strain of coronavirus. This paper proposes a novel model termed “deep fractional max pooling neural network (DFMPNN)” to diagnose COVID-19 more efficiently.Methods: This 12-layer DFMPNN replaces max pooling (MP) and average pooling (AP) in ordinary neural networks with the help of a novel pooling method called “fractional max-pooling” (FMP). In addition, multiple-way data augmentation (DA) is employed to reduce overfitting. Model averaging (MA) is used to reduce randomness.Results: We ran our algorithm on a four-category dataset that contained COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis (SPT), and healthy control (HC). The 10 runs on the test set show that the micro-averaged F1 (MAF) score of our DFMPNN is 95.88%.Discussions: This proposed DFMPNN is superior to 10 state-of-the-art models. Besides, FMP outperforms traditional MP, AP, and L2-norm pooling (L2P).

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Section: Experiments Results and Discussionmentioning

confidence: 99%

“…Therefore, scholars nowadays favor using artificial intelligence (AI) and modern deep learning (DL) to assist radiologists in recognizing COVID-19. Yao (8)…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

Wang

Satapathy

Anderson

et al. 2021

Front. Public Health

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“…We compare the proposed method “MIDCAN” with 8 state-of-the-art methods: GLCM [7] , WE-BBO [8] , WRE [9] , FSVC [10] , OTLS [11] , MRA [13] , GG [14] , SMO [15] . Those methods were carried out on single modality dataset depending on their original paper reported (either CCT or CXR), so we test those methods in the corresponding SIDCAN models and single-modality dataset.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

“…The authors then used support vector machine (SVM) as the classifier. Yao (2020) [8] combined wavelet entropy (WE) and biogeography-based optimization (BBO). Wu (2020) [9] presented a novel method—wavelet Renyi entropy (WRE) to help diagnose COVID-19.…”

Section: Literature Surveymentioning

confidence: 99%

MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray

Zhang

et al. 2021

Pattern Recognition Letters

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Background COVID-19 has caused 3.34m deaths till 13/May/2021. It is now still causing confirmed cases and ongoing deaths every day. Method This study investigated whether fusing chest CT with chest X-ray can help improve the AI's diagnosis performance. Data harmonization is employed to make a homogeneous dataset. We create an end-to-end multiple-input deep convolutional attention network (MIDCAN) by using the convolutional block attention module (CBAM). One input of our model receives 3D chest CT image, and other input receives 2D X-ray image. Besides, multiple-way data augmentation is used to generate fake data on training set. Grad-CAM is used to give explainable heatmap. Results The proposed MIDCAN achieves a sensitivity of 98.10±1.88%, a specificity of 97.95±2.26%, and an accuracy of 98.02±1.35%. Conclusion Our MIDCAN method provides better results than 8 state-of-the-art approaches. We demonstrate the using multiple modalities can achieve better results than individual modality. Also, we demonstrate that CBAM can help improve the diagnosis performance.

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Covid-19 Detection by Wavelet Entropy and Jaya

Wang

2021

Intelligent Computing Theories and Application

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COVID-19 Detection via Wavelet Entropy and Biogeography-Based Optimization

Cited by 19 publications

References 32 publications

RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray

Covid-19 Detection by Wavelet Entropy and Jaya

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